Abstract
BackgroundDepression is one of the most prevalent and disturbing non-motor symptoms in Parkinson’s disease (PD), with few dynamic functional connectivity (dFC) features measured in previous studies. Our aim was to investigate the alterations of the dynamics in de novo patients with PD with depression (dPD).MethodsWe performed dFC analysis on the data of resting-state functional MRI from 21 de novo dPD, 34 de novo patients with PD without depression (ndPD), and 43 healthy controls (HCs). Group independent component analysis, a sliding window approach, followed by k-means clustering were conducted to assess functional connectivity states (which represented highly structured connectivity patterns reoccurring over time) and temporal properties for comparison between groups. We further performed dynamic graph-theoretical analysis to examine the variability of topological metrics.ResultsFour distinct functional connectivity states were clustered via dFC analysis. Compared to patients with ndPD and HCs, patients with dPD showed increased fractional time and mean dwell time in state 2, characterized by default mode network (DMN)-dominated and cognitive executive network (CEN)-disconnected patterns. Besides, compared to HCs, patients with dPD and patients with ndPD both showed weaker dynamic connectivity within the sensorimotor network (SMN) in state 4, a regionally densely connected state. We additionally observed that patients with dPD presented less variability in the local efficiency of the network.ConclusionsOur study demonstrated that altered network connection over time, mainly involving the DMN and CEN, with abnormal dynamic graph properties, may contribute to the presence of depression in patients with PD.
Highlights
Parkinson’s disease (PD) is the second most prevalent neurodegenerative disorder (Poewe et al, 2017)
Previous studies employing the amplitude of low-frequency fluctuation (ALFF) and regional homogeneity (ReHo) approaches revealed that aberrant regional brain activity in several specific areas of prefrontal and cingulate cortices was associated with depressive symptoms in PD (Wen et al, 2013; Luo et al, 2014; Sheng et al, 2014)
More studies focused on large-scale neural networks analysis and found that patients with dPD showed altered intra- and inter-network connectivity, with the involvement of the cognitive executive network (CEN), default mode network (DMN), and basal ganglia network (BGN) (Wei et al, 2017; Liao et al, 2020; Lin et al, 2020)
Summary
Parkinson’s disease (PD) is the second most prevalent neurodegenerative disorder (Poewe et al, 2017). It is widely acknowledged that PD is characterized by motor features and by a multitude of non-motor symptoms, such as depression, of which prevalence is approximately 35% (Reijnders et al, 2008). More studies focused on large-scale neural networks analysis and found that patients with dPD showed altered intra- and inter-network connectivity, with the involvement of the cognitive executive network (CEN), default mode network (DMN), and basal ganglia network (BGN) (Wei et al, 2017; Liao et al, 2020; Lin et al, 2020). Depression is one of the most prevalent and disturbing non-motor symptoms in Parkinson’s disease (PD), with few dynamic functional connectivity (dFC) features measured in previous studies. Our aim was to investigate the alterations of the dynamics in de novo patients with PD with depression (dPD)
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